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| /* | |
| * Copyright 2026 Kyriakos Georgiopoulos | |
| * | |
| * Licensed under the Apache License, Version 2.0 (the "License"); | |
| * you may not use this file except in compliance with the License. | |
| * You may obtain a copy of the License at | |
| * | |
| * http://www.apache.org/licenses/LICENSE-2.0 | |
| * | |
| * Unless required by applicable law or agreed to in writing, software |
| --- | |
| name: code-review | |
| description: Review a pull request (or the current branch) with TWO parallel reviewer subagents on gpt-5.5(high) — a standard reviewer and an adversarial reviewer. Merge their findings only after both return; deliver an in-chat summary always and post to the PR only with consent. When reviewing your own branch, iterate on the feedback and re-invoke until no findings remain. | |
| disable-model-invocation: false | |
| tags: | |
| - productivity | |
| polytoken: true | |
| --- | |
| # Code Review |
| — Да? | |
| — Алё! | |
| — Да да? | |
| — Ну как там с деньгами? | |
| — А? | |
| — Как с деньгами-то там? | |
| — Чё с деньгами? | |
| — Чё? | |
| — Куда ты звонишь? |
Who this is for: an AI agent (or engineer) helping someone build an illustrated, animated floorplan dashboard for Home Assistant from scratch, with their own house, their own illustrations, their own sensors.
How to read it: it is written as questions and answers. Find the question the user is really asking and answer from that section. The numbers, room names and entity IDs here are placeholders — every one of them will be different in a new build. What transfers is the method: the canvas rule, the file naming scheme,
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| name | japanese-tech-writing |
|---|---|
| description | 日本語の技術文書・書籍原稿の文章規範。段落と論証の構成(パラグラフライティング)、論証の厳密さ(ツッコミどころの除去)、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、翻訳調の比喩と擬人化の禁止(「運ぶ」「効く」「開かれた問い」など)、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。 |
| license | Unlicense(https://gist.github.com/k16shikano/67625f2a7d96e3bbdfae8d571a936063) |
日本語で技術的な原稿(書籍の章、記事、解説文)を書く・推敲するときは、以下の規範に従う。


